Beyond the Band-Aid: Reimagining Healthcare Workflows for the AI Era
Sundar Subramanian, CEO of Zyter TruCare
Industry leaders have been rapidly deploying AI programs, many of them hoping for a cure-all for operational and technological maladies of all kinds. Their optimism is not surprising, as tech vendors promising productivity gains, cost savings, and reimagined workflows have frequently sold it as such. But what many organizations have been deploying can be likened ... Read More
<img width="300" height="300" src="https://hitconsultant.net/wp-content/uploads/2026/01/Sundar-Subramanian-CEO-Zyter-TruCare-1.jpg" alt="" class="wp-image-93644" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/Sundar-Subramanian-CEO-Zyter-TruCare-1.jpg 300w, https://hitconsultant.net/wp-content/uploads/2026/01/Sundar-Subramanian-CEO-Zyter-TruCare-1-290x290.jpg 290w, https://hitconsultant.net/wp-content/uploads/2026/01/Sundar-Subramanian-CEO-Zyter-TruCare-1-100x100.jpg 100w" sizes="(max-width: 300px) 100vw, 300px" /> Sundar Subramanian, CEO of Zyter/TruCare Industry leaders have been rapidly deploying AI programs, many of them hoping for a cure-all for operational and technological maladies of all kinds. Their optimism is not surprising, as tech vendors promising productivity gains, cost savings, and reimagined workflows have frequently sold it as such. But what many organizations have been deploying can be likened to a Band-Aid, not a genuine or sustainable remedy for structural problems in their operations and technology stacks. In my years of advising Fortune 500 companies on growth strategy, digital transformation, and operating model redesign, I learned that far too many are trying to use AI as a bandage on outdated systems, organizational silos and outdated processes. Too few realize that the processes underlying the technology need to be reimagined, in order to realize the full potential of AI. This is because, as powerful as it is, AI can’t fix a system that is fundamentally broken. It’s just a tool that amplifies the strengths and weaknesses of the work systems it touches. The Band-Aid Approach In advising organizations on their digital transformation strategies, I have seen AI deployed in the hope it will act as a silver bullet, only to see the technology backfire. For example, Chatbots to improve customer service: Some businesses have deployed conversational AI programs to relieve the burden on call centers. In theory, this could mean shorter wait times and lower call volumes. But when chatbots are bolted onto disconnected CRM systems and antiquated databases, they can only field the simplest questions. Any customer with a more complicated question is immediately put back in the line and back on hold. Even with AI in the mix, the situation is as frustrating and dysfunctional as ever. AI for supply chain management: AI can make a major difference in demand forecasting, but only if forecasts can be trusted. When AI is used on fragmented, incomplete, or outdated data streams, the output is often unreliable. Without addressing upstream data governance, using AI for demand forecasting can be compared to putting new tires on a car with a burned-out engine. They won’t take you very far. This basic pattern can be seen in various industries, and it is because organizations are applying AI to existing workflows rather than rewiring those workflows. In instances like these, AI is actually magnifying inefficiencies instead of eliminating them. AI as a Core Design Principle Organizations must stop trying to use AI as a patch and begin treating it as a core design principle to reimagine and future-proof their operations. In customer service, redesigning the entire support experience around AI-powered self-service, integrating natural language search with unified customer data, and creating seamless escalation paths to human agents are ways to go beyond the Band-Aid approach. Instead of a chatbot grafted onto a call center, the system is transformed into an intelligent, end-to-end support ecosystem. In supply chain management, instead of layering forecasting AI on top of fragmented systems, organizations can build centralized, AI-ready data platforms. These systems would integrate supplier, logistics, and sales data in real time, giving forecasting models accurate inputs. Forecasting can then become faster while also being predictive and actionable. Real change requires more than new technology. It requires new thinking. Questioning long-held assumptions about how work is done and how teams are structured is a good first step. This is easier said than done, especially for organizations where workflows have been developed over long periods of time. But new thinking is increasingly necessary, and is the only hope of turning around the dismal stats on satisfaction with AI deployments (and return on AI investments) among IT leaders. How to Rip Off the Band-Aid For organizations ready to go beyond the Band-Aid approach and experience the fundamental change that AI can bring, there are several practical steps to take: Understand your trouble areas before deploying AI: Map out your organization’s workflows and identify as many root causes of inefficiencies as possible, as this is smarter than simply tracking the symptoms. Ensure your data is ready: Make sure that AI has access to pristine, consistent, and integrated data streams. This is the way to generate output that can be trusted. Align people and processes: Redesign roles, responsibilities, and incentives within your organization to prepare for a future of AI-enabled workflows. Organizations that stick with a piecemeal approach to automation risk falling behind rivals that outperform them with workflows fundamentally reimagined for the AI era. They are likely to remain dissatisfied with their AI rollout and struggle to see a clear return on investment. Organizations that add automation to dysfunctional systems of work will miss out on AI’s full potential. Those that rethink their operations from first principles and put AI in the center of the equation are those poised to prosper in the future. About Sundar Subramanian Sundar Subramanian is the Chief Executive Officer (CEO) of Zyter|TruCare, a population health management technology and services company, appointed in mid-2025 to lead its focus on AI-driven transformation and integration of technology with expert services for health plans. He brings extensive experience from PwC (Strategy&), McKinsey & Company, and WellCare Health Plans, specializing in strategy, digital transformation, and operating model redesign in the healthcare sector.
Rewriting the Front Door: How AI-Assisted Intake Solves the 2026 Mental Health Crisis
Alexander Amatus, Business Development Lead at TherapyNearMe.com.au
For all the hype about AI “replacing therapists,” the more immediate and profound shift is happening somewhere far less glamorous: the intake process. Across health systems, primary care physicians, insurers, and community providers are still funnelling patients into behavioural health through faxed referrals, phone tag, and unstructured web forms. Patients finally find the courage to ... Read More
<img loading="lazy" src="https://hitconsultant.net/wp-content/uploads/2026/01/53CAECE0-3B21-403E-8122-1F7F54995053.jpeg" alt="Rewriting the Front Door: How AI-Assisted Intake Solves the 2026 Mental Health Crisis" class="wp-image-93647" width="710" height="711" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/53CAECE0-3B21-403E-8122-1F7F54995053.jpeg 1125w, https://hitconsultant.net/wp-content/uploads/2026/01/53CAECE0-3B21-403E-8122-1F7F54995053-300x300.jpeg 300w, https://hitconsultant.net/wp-content/uploads/2026/01/53CAECE0-3B21-403E-8122-1F7F54995053-290x290.jpeg 290w, https://hitconsultant.net/wp-content/uploads/2026/01/53CAECE0-3B21-403E-8122-1F7F54995053-768x769.jpeg 768w, https://hitconsultant.net/wp-content/uploads/2026/01/53CAECE0-3B21-403E-8122-1F7F54995053-100x100.jpeg 100w" sizes="(max-width: 710px) 100vw, 710px" /> Alexander Amatus, Business Development Lead at TherapyNearMe.com.au For all the hype about AI “replacing therapists,” the more immediate and profound shift is happening somewhere far less glamorous: the intake process. Across health systems, primary care physicians, insurers, and community providers are still funnelling patients into behavioural health through faxed referrals, phone tag, and unstructured web forms. Patients finally find the courage to ask for help, then hit voicemail, long waits, and confusing eligibility rules. In many organisations, no one can even answer a basic question: “How many people quietly give up before their first appointment?” Generative AI and conversational interfaces will not solve the workforce shortage in mental health. But they are about to rewrite the front door: how demand is captured, triaged, and matched to scarce clinical capacity. If we get this right, AI assistants can stop squandering patient courage and clinician time. If we get it wrong, we risk building a slicker version of the same broken funnel, now with new safety and equity problems. The question for healthcare leaders is no longer “Should we use AI in behavioural health?” Instead, it’s: “How do we design AI-assisted intake that clinicians can trust, regulators can live with, and patients can actually benefit from?” The Status Quo Is Operationally and Clinically Unsustainable The demand–supply gap in mental health is well documented. The World Health Organization estimates that depression and anxiety alone cost the global economy over US$1 trillion per year in lost productivity, while more than 70% of people with mental health conditions receive no treatment in many countries. Even in better-resourced systems, behavioural health access is constrained not just by clinician headcount but by operational friction: Referrals arrive as free-text letters, PDFs, and faxes that require manual triage. Intake staff chase patients by phone, often unsuccessfully, because anxious or ambivalent patients screen unknown numbers. Limited appointment slots are filled on a “next available” basis, not on fit or risk, leading to no-shows and early dropout. While patients wait, there is often no structured psychoeducation, risk monitoring, or navigation support. The downstream consequences are predictable: overwhelmed emergency departments, fragmented care coordination, clinician burnout, and widening inequities as digitally savvy and persistent patients fare better than those who are not. In this context, doing nothing about AI is not neutral. It is an active choice to leave a high-friction, low-visibility intake funnel untouched while workloads and expectations increase. What an AI Mental Health Intake Assistant Actually Does The most promising use case for AI in behavioural health today is not direct-to-consumer “therapy chatbots,” but clinic-integrated assistants that sit between referral and first appointment. In practice, an AI-assisted intake workflow can look like this: Referral or self-referral triggers an invitation When a referral is created in an EHR or referral management platform, the patient automatically receives a secure link to complete an AI-guided intake at a time and place that feels safe to them. Conversational, structured data collection Instead of a static form, the assistant uses natural language to collect information on presenting concerns, risk factors, funding / insurance status, practical constraints (location, modality, language), and clinically relevant preferences. Under the hood, free-text input is mapped into structured fields and a concise narrative. Human-in-the-loop triage and matching Clinicians or trained intake staff review the AI-generated summary, override any misclassifications, and match patients to appropriate services and providers based on skill set, risk, and availability. Pre-appointment education and expectation setting The same assistant can send plain-language explanations of what to expect in the first session, how telehealth works, and how to prepare. Early evidence suggests that clear pre-treatment information can reduce anxiety and no-show rates in psychotherapy. Feedback to referrers and population-level insight Aggregated data allows health systems to answer questions they currently guess at: which referral sources see the longest waits, which regions show rising risk, which service lines are chronically overloaded. This is not “therapy.” It is workflow transformation at the fragile point where motivation is highest and systems are most disorganised. The Clinical and Ethical Risks Are Real—and Manageable Understandably, clinicians and regulators are wary. Recent analyses have shown that unsupervised mental health chatbots can provide inaccurate or even unsafe responses, including inadequate handling of suicidal ideation. Those concerns are valid, but they are arguments for designing guardrails, not for ignoring the opportunity. At a minimum, AI-assisted intake in behavioural health needs: Explicit scope limits The assistant must clearly state that it does not provide diagnosis, treatment, or crisis care, and should never position itself as a “therapist.” This is consistent with emerging guidance from professional bodies on AI in mental health. Crisis deflection, not crisis management If a patient indicates acute risk, the system’s job is to stop, provide crisis resources, and trigger human follow-up according to local protocols, not to attempt de-escalation on its own. Mandatory human review for risk and allocation AI can draft, but humans must decide. Any risk flagging, urgency scoring, or service recommendations generated by the assistant should be treated as suggestions, not decisions. Robust privacy and data governance Intake conversations will often include some of the most sensitive information a patient ever shares. Data minimisation, encryption, audit trails, and explicit consent for secondary uses (such as model improvement) are non-negotiable. Equity monitoring Health systems must watch for differential performance across languages, cultures, age groups, and disability status. If AI-assisted intake works well for urban, higher-literacy users and poorly for others, it may widen disparities rather than narrowing them. Without these safeguards, AI intake is rightly a non-starter. With them, it becomes a controllable, auditable component of the care pathway, arguably more transparent than the ad hoc phone calls and paper forms it replaces. Operational ROI: What Healthcare Leaders Should Expect (and Not Expect) Health systems evaluating AI intake tools should be suspicious of magical thinking. An assistant at the front door will not “fix mental health” or suddenly double clinician capacity. More realistic outcome targets include: Reduced administrative load Intake staff spend less time chasing patients and re-keying data, and more time on high-value tasks like complex case coordination. Improved match quality and throughput Better structured data and matching logic can reduce misallocated referrals and first-session “wrong fit” encounters, which waste capacity for everyone. Higher conversion from referral to first appointment Making it easier to complete intake when motivation peaks should increase the proportion of referred patients who actually start care. Better visibility for planning and contracting Aggregated intake data can support capacity planning, value-based contracts, and targeted investment in undersupplied service lines. What AI should not be expected to do is deliver clinical outcomes in isolation. Its role is to get the right patient to the right human care, sooner and with better information. The Next Frontier: Powering AI Intake Sustainably There is an emerging, often overlooked dimension to this conversation: energy. Large language models are compute-intensive. As health systems scale up AI across imaging, documentation, and now intake, energy consumption and associated emissions are becoming a material part of digital health’s footprint. Recent estimates suggest that AI workloads could significantly increase data centre energy demand if left unmanaged. Behavioural health leaders cannot solve climate policy alone, but they can: Ask vendors direct questions about energy usage and mitigation. Prefer solutions that run efficiently and, where possible, leverage renewable-powered infrastructure. Treat sustainability as part of the risk/benefit assessment, not an afterthought. An intake assistant that improves access but quietly drives up emissions at scale is not aligned with the broader mission of promoting health. A Strategic Choice, Not a Gadget AI in mental health evokes strong emotions, and rightly so. At stake are vulnerable patients, overextended clinicians, and delicate trust relationships that are easy to damage and hard to rebuild. But the choice health systems face is not between “AI or no AI.” It is between: AI deployed deliberately at the front door, with clear guardrails, human oversight, and governance; or AI is deployed indirectly and chaotically, as patients turn to unvetted consumer chatbots because the official intake pathways remain opaque and unresponsive. The first path allows health leaders, payers, and regulators to shape how conversational AI supports behavioural healthcare. The second leaves that shape consumer tech platforms and marketing algorithms. If we believe that the moment a patient reaches for help should be treated as a scarce, high-value clinical event, then AI-assisted intake is not a gadget. It is a strategic lever—one that should be pulled with care, but pulled all the same. About Alexander Amatus Alexander Amatus is Business Development Lead at TherapyNearMe.com.au, Australia’s fastest growing national mental health service. He works at the intersection of clinical operations, AI-enabled care pathways, and sustainable digital infrastructure.
Utah Becomes First State to Approve AI System for Prescription Renewals
What You Should Know – The Utah Department of Commerce has officially authorized Doctronic to become the first AI system in the U.S. to legally participate in medical decision-making for prescription renewals. – Operating under Utah’s “Regulatory Sandbox” framework, the pilot aims to address the $100 billion in avoidable medical expenses caused annually by medication ... Read More
<img loading="lazy" width="1200" height="800" src="https://hitconsultant.net/wp-content/uploads/2026/01/image-3.png" alt="Utah Becomes First State to Approve AI System for Prescription Renewals" class="wp-image-93707" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/image-3.png 1200w, https://hitconsultant.net/wp-content/uploads/2026/01/image-3-300x200.png 300w, https://hitconsultant.net/wp-content/uploads/2026/01/image-3-290x193.png 290w, https://hitconsultant.net/wp-content/uploads/2026/01/image-3-768x512.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> What You Should Know – The Utah Department of Commerce has officially authorized Doctronic to become the first AI system in the U.S. to legally participate in medical decision-making for prescription renewals. – Operating under Utah’s “Regulatory Sandbox” framework, the pilot aims to address the $100 billion in avoidable medical expenses caused annually by medication non-compliance, specifically targeting the 80% of medication activity that involves routine refills. The “Regulatory Sandbox”: A New Frontier for Autonomous AI While the industry has been fixated on “ambient speech” (which currently sees 79% adoption for note-taking), Utah is skipping ahead to autonomous medical decision-making. Utah’s Office of Artificial Intelligence Policy (OAIP) is utilizing a first-in-the-nation mandate to provide temporary regulatory relief. This allows Doctronic’s “multi-agent AI health platform” to: Legally Prescribe: Independently evaluate and authorize routine refills for chronic conditions. Orchestrate Care: Integrate with centralized medical records and a nationwide physician network to provide asynchronous evaluation. Test Policy: Provide real-world data to help states like Arizona, Texas, and Wyoming—which are currently developing their own AI Sandboxes—craft future legislation. Utah’s Regulatory Sandbox Model and National Implications Utah’s Office of Artificial Intelligence Policy represents itself as a first-in-the-nation entity specifically created to establish frameworks for AI technology deployment across regulated industries. The office operates under legislative authority to create regulatory mitigation agreements—formal arrangements that provide temporary relief from existing regulations to enable controlled innovation testing. Senator Kirk Cullimore, who sponsored the enabling legislation, frames the approach around a “doctor, not device” principle—ensuring automation supports rather than replaces human clinical judgment. This framing attempts to thread a political needle: embracing technological innovation while addressing concerns about depersonalized care and physician displacement. The AI sandbox model includes several protective features. Participants operate under enhanced monitoring requirements, must report safety metrics and outcomes data, face clear termination triggers if problems emerge, and provide findings publicly to inform broader policy development. Utah’s Office of Artificial Intelligence Policy will evaluate Doctronic across multiple dimensions: medication refill timeliness and adherence rates, patient access improvements and satisfaction scores, safety outcomes including adverse events, workflow efficiency gains, and healthcare cost impacts. This measured experimentation approach contrasts sharply with the regulatory vacuum that allowed many digital health tools to proliferate without meaningful oversight. By explicitly authorizing specific AI capabilities while demanding rigorous evaluation, Utah attempts to balance innovation enablement with patient protection. The state has previously established similar partnerships with ElizaChat and Dentacor, suggesting a deliberate strategy to position Utah as a proving ground for healthcare AI. Margaret Woolley Busse, Executive Director of the Utah Department of Commerce, emphasizes the dual objectives: “fostering innovation and ensuring consumer safety” through regulatory frameworks that provide “certainty necessary for companies to develop impactful solutions.” Other states are watching closely. Arizona and Texas have created their own AI sandbox frameworks, Wyoming is developing similar structures, and the announcement specifically notes these parallel efforts as evidence of “growing national push for safe, testable pathways for autonomous AI in regulated industries.” If Utah’s pilot demonstrates positive outcomes without significant safety problems, expect rapid replication across states seeking to attract healthcare technology investment.
Why Tegria’s Exit from Providence Reshapes Health IT Outsourcing
What You Should Know – Providence St. Joseph Health has officially offloaded Tegria Services Group to private equity firm Altaris, marking the end of a high-profile experiment in health system-owned IT consulting. – This move decouples Tegria from its non-profit roots, handing the reigns to a PE player with $9B in assets and a mandate ... Read More
<img loading="lazy" width="1200" height="628" src="https://hitconsultant.net/wp-content/uploads/2021/08/Tegria.jpg" alt="Tegria, Cedar Partner to Humanize the Patient Financial Experience" class="wp-image-62529" srcset="https://hitconsultant.net/wp-content/uploads/2021/08/Tegria.jpg 1200w, https://hitconsultant.net/wp-content/uploads/2021/08/Tegria-300x157.jpg 300w, https://hitconsultant.net/wp-content/uploads/2021/08/Tegria-290x152.jpg 290w, https://hitconsultant.net/wp-content/uploads/2021/08/Tegria-768x402.jpg 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> What You Should Know – Providence St. Joseph Health has officially offloaded Tegria Services Group to private equity firm Altaris, marking the end of a high-profile experiment in health system-owned IT consulting. – This move decouples Tegria from its non-profit roots, handing the reigns to a PE player with $9B in assets and a mandate to scale Tegria’s MEDITECH and Epic optimization services across a fragmented market. The Post-Captive Pivot: Can Tegria Compete Without a Safety Net? For years, Tegria was the “Providence-exclusive” resource—a captive services arm that some rival health systems viewed with skepticism. That’s over. By moving Tegria into the Altaris portfolio, the firm is now a “pure-play” competitor in the HIT outsourcing space. The timing is calculated. 80% of CIOs we’ve surveyed are holding or increasing their professional services spend, even as they hack away at other budget lines. Why? Because the labor market for Epic and MEDITECH talent is broken. CIOs aren’t hiring; they’re “renting” expertise to keep their heads above water. This is a strategic exit. Providence is cleaning up its balance sheet (following the 2024 Acclara sale), and Altaris is betting that Tegria’s Best in KLAS status in Payer IT and MEDITECH hosting can be weaponized to steal market share from generalist firms like Deloitte.
Healthconnect Texas and PCIC Unify to Build the Ultimate Social Drivers of Health (SDoH) Infrastructure
What You Should Know – Healthconnect Texas and the Patient Care Intervention Center (PCIC) have announced a strategic unification, merging statewide clinical connectivity with deep community-based data on non-medical drivers of health. – The unified infrastructure aims to provide a “360-degree view” of patient wellness, enabling providers and policymakers to address the 80-90% of health ... Read More
<img loading="lazy" width="800" height="400" src="https://hitconsultant.net/wp-content/uploads/2026/01/Healthconnect-Texas-Logo.jpg" alt="" class="wp-image-93702" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/Healthconnect-Texas-Logo.jpg 800w, https://hitconsultant.net/wp-content/uploads/2026/01/Healthconnect-Texas-Logo-300x150.jpg 300w, https://hitconsultant.net/wp-content/uploads/2026/01/Healthconnect-Texas-Logo-290x145.jpg 290w, https://hitconsultant.net/wp-content/uploads/2026/01/Healthconnect-Texas-Logo-768x384.jpg 768w" sizes="(max-width: 800px) 100vw, 800px" /> What You Should Know – Healthconnect Texas and the Patient Care Intervention Center (PCIC) have announced a strategic unification, merging statewide clinical connectivity with deep community-based data on non-medical drivers of health. – The unified infrastructure aims to provide a “360-degree view” of patient wellness, enabling providers and policymakers to address the 80-90% of health outcomes driven by lifestyle and environmental factors that typically live outside the Electronic Health Record (EHR). The Interoperability Crisis: Beyond the Four Walls of the Hospital For years, the “Vaporware” in interoperability has been the promise of SDoH integration. While EHR vendors have made progress in provider-to-provider record sharing, clinician satisfaction remains poor due to “poor data mapping” and a deluge of “unusable data”. The Healthconnect-PCIC unification addresses this structural failure by: Merging Clinical & Community Streams: Combining real-time clinical data from Healthconnect Texas with PCIC’s insights into housing, nutrition, and transportation. Reducing Administrative Churn: Automating the exchange of records between clinical and community-based organizations to facilitate whole-person care coordination. Fueling Actionable Insights: Providing researchers and policymakers with a unified dataset to identify and reduce health disparities across diverse Texas communities. The Trust Hurdle While the technical unification is a massive step, the real barrier isn’t the API—it’s trust. KLAS data confirms that in payer-provider data sharing, 96% of successful case studies cite “building trust” as a best practice, while only 48% cite “strong technology”. For this Texas initiative to succeed, the unified organization must prove it can protect patient privacy while making data “liquid” enough to be useful. The Roadmap: What’s Next for Texas? The unified organization will begin operating under an integrated model immediately, with new joint initiatives and service offerings slated for early 2026. For C-suite leaders at Texas health systems, this unification offers a “plug-and-play” infrastructure to meet the increasingly strict CMS mandates (like CMS-0057-F) regarding data sharing and health equity. The Bottom Line By closing the gap between clinical data and community context, Healthconnect Texas and PCIC are moving interoperability from a “technical requirement” to a “strategic asset” for the 30 million people they serve.
Who Made Your Medicine? How to Use ProPublica’s Rx Inspector to Audit Your Generics
What You Should Know – ProPublica has released Rx Inspector, a first-of-its-kind database that allows Americans to identify the exact facility where their generic prescriptions were manufactured. – By connecting disparate FDA databases and suing for factory location data, ProPublica has mapped more than 80% of generic products to their originating plants, revealing safety violations ... Read More
<img loading="lazy" width="752" height="385" src="https://hitconsultant.net/wp-content/uploads/2026/01/image-2.png" alt="Who Made Your Medicine? How to Use ProPublica’s Rx Inspector to Audit Your Generics" class="wp-image-93699" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/image-2.png 752w, https://hitconsultant.net/wp-content/uploads/2026/01/image-2-300x154.png 300w, https://hitconsultant.net/wp-content/uploads/2026/01/image-2-290x148.png 290w, https://hitconsultant.net/wp-content/uploads/2026/01/image-2-750x385.png 750w" sizes="(max-width: 752px) 100vw, 752px" /> What You Should Know – ProPublica has released Rx Inspector, a first-of-its-kind database that allows Americans to identify the exact facility where their generic prescriptions were manufactured. – By connecting disparate FDA databases and suing for factory location data, ProPublica has mapped more than 80% of generic products to their originating plants, revealing safety violations that the FDA has historically kept hidden from consumers. The Shadow World of Generic Manufacturing Generic drugs now account for 90% of all prescriptions dispensed in the U.S., yet their origins remain a “black box” for consumers and clinicians alike. Labels on pill bottles typically list only the distributor or repackager, masking the fact that the underlying medication may have been produced in facilities with documented issues such as mold, filthy equipment, or insect infestations. To build Rx Inspector, journalists connected disparate databases and sued the FDA in federal court to obtain factory location lists that the agency previously deemed “confidential commercial information”. Operational Mechanics: How to Audit Your Prescription The tool acts as a “digital auditor” for the patient’s medicine cabinet. Users navigate the system using: The “Front Door”: Users enter the drug name and the company listed on the pill bottle (repackager/distributor). Advanced Identification: To ensure a precise match, users can filter by pill color, shape, imprints, or the National Drug Code (NDC). The Outcome: The app displays the facility address, other drugs produced there, and available FDA inspection reports. The ‘Risk Tier’ Framework: Decoding FDA Compliance Rx Inspector translates technical FDA compliance designations into actionable insights for patients and providers: Import Alert: Highest Risk. The facility is banned from shipping to the U.S. due to severe standard violations, though “exemptions” are sometimes granted to prevent shortages. Warning Letter: Disciplinary action highlighting significant federal violations; requires a formal company response. Unacceptable Compliance: “Official Action Indicated” (OAI). Serious issues found; the FDA typically pauses new drug approvals from this facility. Voluntary Action (VAI): Most Common. Issues were found, but the FDA determined the facility could correct them voluntarily. Inspector Issues (483): Specific deficiencies documented via a Form 483; these may not appear in official summary dashboards. Acceptable State (NAI): No Action Indicated.” The best possible outcome; no compliance action taken. Do not confuse “transparency” with “crisis.” While Rx Inspector highlights troubling inspection reports, doctors and pharmacists strongly advise patients not to stop taking medications. Instead, use this data to engage your provider in a discussion about alternative manufacturers or pharmacies if a facility has a pattern of “Official Action Indicated” reports. The “Rx Roulette” Methodology The database is built on a complex architecture that connects the Structured Product Labeling database, the National Drug Code (NDC) Directory, and years of Form 483 documents obtained via Freedom of Information Act (FOIA) requests. While the data is a “first step” and may have gaps due to corporate acquisitions, it represents the most comprehensive public audit of the generic drug supply chain to date. The Bottom Line: We have officially moved beyond the era of “trust the label.” For 2026, the mandate is clear: Verify the source.
The Future of the AI-Enabled Health Consumer in 2026
Glen Tullman, Chief Executive Officer, Transcarent
Imagine a world where your healthcare simply works. Everything you need to manage your health lives on your phone, in one place. It speaks your language, reflects your preferences, and connects you to care instantly—day or night, weekday, weekend, or holiday. You never sit on hold. You don’t wait six weeks for an appointment. You ... Read More
<img loading="lazy" width="693" height="746" src="https://hitconsultant.net/wp-content/uploads/2026/01/Glenn-Tullman.jpg" alt="" class="wp-image-93641" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/Glenn-Tullman.jpg 693w, https://hitconsultant.net/wp-content/uploads/2026/01/Glenn-Tullman-279x300.jpg 279w, https://hitconsultant.net/wp-content/uploads/2026/01/Glenn-Tullman-269x290.jpg 269w" sizes="(max-width: 693px) 100vw, 693px" /> Glen Tullman, Chief Executive Officer, Transcarent Imagine a world where your healthcare simply works. Everything you need to manage your health lives on your phone, in one place. It speaks your language, reflects your preferences, and connects you to care instantly—day or night, weekday, weekend, or holiday. You never sit on hold. You don’t wait six weeks for an appointment. You never wonder if a doctor is in your network. You never guess whether to head to the ER, urgent care, or connect virtually. You always know what’s covered, what’s next, and why. That world isn’t science fiction. It’s within reach. AI Makes Healthcare About You Healthcare has always been personal but rarely has it felt personalized. It’s about a mindset shift—designing healthcare around people instead of institutions. And I believe Artificial intelligence offers a chance to reverse that equation. AI is already transforming how we detect diseases, interpret images, and discover new therapies. But its most profound impact may come from something simpler: help people learn about their health, understand their options, and confidently make decisions. AI can learn from your health history, your communication preferences, your behaviors, and even your goals. It can connect the dots between your physical, mental, and financial health in a way no human system could ever scale. When done right, this isn’t about replacing clinicians, and frankly, we just don’t have enough of them. It’s about allowing them to deliver care of the highest value. It’s about making every interaction smarter, faster, and more compassionate. AI can help us reimagine healthcare as a continuous, connected experience rather than a series of isolated transactions. We live 99 percent of our lives outside of the four walls of a doctor’s office, and our tools need to reflect that. AI can serve as a trusted guide that listens, learns, and acts in real time, helping people make decisions with confidence and clarity on their terms. A Consumer-Directed Model for the Digital Age In the future, AI will quietly coordinate the complex work behind the scenes so people can focus on their well-being, not the system’s complexity. It will help predict when you might need care before you realize it yourself. It will ensure preventive screenings happen on time, guide you toward affordable options, and provide transparent explanations for what you are being asked to pay and why. Most importantly, it will learn from every interaction to better reflect who you are. Your healthcare experience will feel as familiar and personal as the other technologies you rely on every day, but this one will actually care about your health. Trust Is the Foundation For all its potential, no technology will succeed in healthcare without trust. Consumers must know that their most sensitive information is safe. The future we’re describing isn’t one where your data is scattered across apps and servers; it’s one where everything you share lives in a secure, HIPAA-protected environment. You deserve to have the convenience of personalized, real-time guidance without worrying about who has access to your medical history or private health data. The system of the future will protect your confidentiality as fiercely as it protects your health, giving people confidence to engage fully without fear of exposure or misuse. Trust and privacy are not afterthoughts; they are the foundation that makes this transformation possible. System That Anticipates, Not Reacts In today’s healthcare, we often wait until something goes wrong. In tomorrow’s, care will anticipate what’s next. By combining AI with human clinical expertise, we can finally move from reactive, episodic care to proactive, connected health. We can empower consumers to make smarter choices, reduce waste, and improve outcomes — all while lowering costs for all health consumers, that’s you and I, our employers and health plans. Imagine an intelligent system that knows when you’re due for a screening and schedules it for you. That notices subtle changes in your patterns—sleep, activity, glucose, mood—and nudges you toward support before a small problem becomes a crisis. That helps you understand not only what care you need, but why and where to get it most affordably and effectively. AI will make prevention proactive. It will make follow-up automatic. And it will turn the maze of healthcare into a guided journey that adapts to you in real time. A Future Within Reach For too long, healthcare innovation has been about adding more—more portals, more passwords, more confusion. The real breakthrough will come from subtraction. Simplifying. Streamlining. Centering everything around the consumer experience. The AI-enabled healthcare future will not be defined by one company, one technology, or one breakthrough. It will be defined by a new standard of simplicity and humanity, one where you don’t even notice the technology; you just appreciate the ease of the experience. The most innovative thing we can do in healthcare isn’t to build smarter machines. It’s to build a smarter system—one that finally feels like it was designed for the people it serves. The Possible Future If we get this right, the future healthcare consumer will be empowered, not overwhelmed. They’ll have the information, confidence, and tools to make smart decisions about their health without navigating a system that too often feels adversarial. They’ll have one trusted place to turn for guidance, and behind that simplicity will be intelligence that anticipates their needs and supports their choices. That’s the vision we should be building toward—a healthcare experience that feels seamless, personalized, and human, powered by technology that finally understands what care means This future isn’t theoretical. It’s being built right now because we simply can’t wait, it’s not an option. About Glen Tullman Glen Tullman is the Chief Executive Officer of Transcarent, the first comprehensive, consumer-directed health and care platform that makes it easy for people to access high-quality, affordable health and care. Transcarent is transforming healthcare with WayFinding, an experience powered by clinicians and generative AI where Members can “Just Ask Transcarent” to instantly get trusted health and benefits navigation, take the next step with personalized clinical guidance, and access on-demand care delivery. Tullman is the former Executive Chairman, Chief Executive Officer, and Founder of Livongo Health, the first at-scale consumer digital health company to truly empower people with chronic conditions to live better and healthier lives using data science. In his final year, Glen led Livongo through the largest consumer digital health Initial Public Offering in history, a secondary offering, a convertible debt offering that raised over $550 million, and the industry’s largest merger to date between Livongo and Teladoc Health, valuing Livongo at $18.5 billion and beginning a new era of consumer-directed virtual care.
The Agentic Era: Why Glen Tullman is Killing the Healthcare Chatbot in 2026
What You Should Know – Transcarent has officially launched WayFinding 2.0, marking a strategic shift from the “chat assistant era” to the “agentic action era” in healthcare. – By integrating autonomous AI agents that can schedule appointments, triage symptoms, and manage follow-ups via a proprietary Total Recall Memory Engine, the platform aims to eliminate the ... Read More
<img loading="lazy" width="1500" height="766" src="https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-1500x766.jpg" alt="" class="wp-image-93682" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-1500x766.jpg 1500w, https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-300x153.jpg 300w, https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-290x148.jpg 290w, https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-768x392.jpg 768w, https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-1536x785.jpg 1536w, https://hitconsultant.net/wp-content/uploads/2026/01/CA_png_flow_w_frame-2048x1046.jpg 2048w" sizes="(max-width: 1500px) 100vw, 1500px" /> What You Should Know – Transcarent has officially launched WayFinding 2.0, marking a strategic shift from the “chat assistant era” to the “agentic action era” in healthcare. – By integrating autonomous AI agents that can schedule appointments, triage symptoms, and manage follow-ups via a proprietary Total Recall Memory Engine, the platform aims to eliminate the chronic administrative friction that currently defines the American healthcare experience. The Death of the Chatbot: Entering the ‘Agentic’ Era For the past three years, healthcare “innovation” has been dominated by large language models (LLMs) that act as sophisticated search engines. Glen Tullman, CEO of Transcarent, is declaring that era over. “We’ve officially moved beyond the ‘chat assistant era’ into the ‘agentic action era’,” Tullman noted during the CES 2026 announcement. Unlike traditional generative AI, which requires a human to prompt it for every step, agentic AI systems autonomously plan, reason, and execute multi-step tasks. In the WayFinding 2.0 ecosystem, this means the AI doesn’t just tell you that you need a doctor; it finds an in-network, high-quality provider, checks your preferences, and books the appointment. Technical Architecture: The ‘Total Recall’ Memory Engine The standout feature of WayFinding 2.0 is its Total Recall Memory Engine, a longitudinal memory layer that learns a member’s specific preferences, past medical history, and behavioral patterns. Persistent Reminders: The system recalls past doctor’s orders to deliver specific next steps and support medication adherence. Personalized Health Paths: AI agents shape a “health path” informed by a network of doctors and therapists, recommending preventive measures and screenings unique to each individual. Clinician-In-The-Loop: To avoid the “hallucination” risks common in generic LLMs, Transcarent’s models are informed by millions of interactions with doctors from its affiliated medical groups. Moving from Pilots to Platforms Industry analysts project healthcare generative AI investments will triple by 2026. Transcarent’s move suggests that the “winners” will be those who move from “chatting” to “doing.” For the 1,700 employers and 35 health plans currently partnering with Transcarent, WayFinding 2.0 represents a shift from “piloting AI” to “deploying an autonomous workforce”. In a market where 11 million health workers will be in shortage by 2030, agentic AI is no longer a luxury—it is an operational necessity.
The Standard of Care Shift: Heartflow Secures Nationwide Aetna Coverage for AI Plaque Analysis
What You Should Know – Heartflow has achieved a major milestone as Aetna updates its policies to cover AI-driven Heartflow Plaque Analysis across all lines of business, effective December 23, 2025. – This move, combined with the new Category I CPT code 75577, provides a clear reimbursement pathway for the first time, establishing 4.00 total ... Read More
<img loading="lazy" width="1500" height="827" src="https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis-1500x827.jpg" alt="" class="wp-image-93688" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis-1500x827.jpg 1500w, https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis-300x165.jpg 300w, https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis-290x160.jpg 290w, https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis-768x423.jpg 768w, https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis-1536x847.jpg 1536w, https://hitconsultant.net/wp-content/uploads/2026/01/Heartflow-Plaque-Analysis.jpg 1720w" sizes="(max-width: 1500px) 100vw, 1500px" /> What You Should Know – Heartflow has achieved a major milestone as Aetna updates its policies to cover AI-driven Heartflow Plaque Analysis across all lines of business, effective December 23, 2025. – This move, combined with the new Category I CPT code 75577, provides a clear reimbursement pathway for the first time, establishing 4.00 total RVUs for the service and ensuring that AI-enabled plaque quantification is no longer a “pilot” technology but a standard of care. The Reimbursement Infrastructure: Category I Validation For years, AI in imaging was hampered by “Category III” status—experimental codes with inconsistent payment. As of January 1, 2026, the American Medical Association (AMA) has officially converted Plaque Analysis to Category I CPT code 75577. Physician Value: Establishes 0.85 work RVUs. Institutional Value: A total of 4.00 RVUs (including technical components) enables hospitals to scale this technology profitably. Aetna Coverage: Specifically covers patients with 1–69% stenosis (mild-to-moderate narrowing) identified on coronary CTA. Clinical Evidence: The DECIDE Registry Heartflow’s expansion is backed by the DECIDE Registry, a prospective study of ~22,000 patients. Treatment Modification: Adding Plaque Analysis to standard CCTA led to medical management changes in more than 50% of patients. Outcome Potential: Changes in therapy led to an average LDL cholesterol decrease of 18.7 mg/dL, associated with a projected 15% reduction in cardiac event risk. The “Gold Standard” Check: The tool maintains 95% agreement with the invasive gold standard, Intravascular Ultrasound (IVUS), in blinded core lab adjudications. Investigational Staging While the Heartflow Plaque Analysis is FDA-cleared, the Heartflow Plaque Staging framework (used in the DECIDE registry) remains an investigational-only framework. Clinicians must distinguish between the cleared quantification tool and the investigational risk-stratification models. Additionally, coverage is strictly for the 1-69% stenosis range; patients with “normal” (0% stenosis) CTA results are generally not covered for this AI add-on. The C-Suite Forecast: 2026 With Aetna, UnitedHealthcare, Cigna, and Humana now on board, the majority of insured lives in the U.S. have access to this technology. For Health System CEOs, the question is no longer about “if” to adopt, but “how” to integrate Heartflow into the cardiology workflow to maximize the new 4.00 RVU payment structure.
How Medable’s Agentic AI TMF Solves the Clinical Trial ‘Clerical Debt’ Problem
What You Should Know – Medable has announced its TMF Agent, a first-of-its-kind agentic AI tool designed to automate the labor-intensive Trial Master File (TMF) process at JPM 2026. It automates 95% of manual document workflows, ingesting and classifying files for Veeva Vault and OpenText. – By reducing the administrative burden that consumes 33% of ... Read More
<img loading="lazy" width="1500" height="248" src="https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-1500x248.jpg" alt="Medable Unveils Generative AI to Accelerate Clinical Trial Startup" class="wp-image-83833" srcset="https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-1500x248.jpg 1500w, https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-300x50.jpg 300w, https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-290x48.jpg 290w, https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-768x127.jpg 768w, https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-1536x254.jpg 1536w, https://hitconsultant.net/wp-content/uploads/2024/11/Medable-AI-2048x339.jpg 2048w" sizes="(max-width: 1500px) 100vw, 1500px" /> What You Should Know – Medable has announced its TMF Agent, a first-of-its-kind agentic AI tool designed to automate the labor-intensive Trial Master File (TMF) process at JPM 2026. It automates 95% of manual document workflows, ingesting and classifying files for Veeva Vault and OpenText. – By reducing the administrative burden that consumes 33% of a CRA’s time, Medable enables “one-day study starts” while maintaining human-in-the-loop (HITL) compliance and audit traceability. The “Clerical Debt” Crisis in Clinical Research Despite the digital transition to eTMFs, internal data suggests that 95% of clinical documents are still processed manually. This creates a “clerical debt” that leads to systemic inconsistencies, backlogs, and high-stress “pre-inspection fire drills”. The TMF Agent is built on Medable’s Agent Studio, which allows for the creation of system-agnostic AI agents that work across existing ecosystems like Veeva Vault, Wingspan, and OpenText. Technical Architecture: The Agentic Workflow Unlike standard automation, Medable’s agentic architecture is designed to handle “unstructured” friction. Autonomous Ingestion: Agents monitor shared inboxes and drives to pull documents the moment they are finalized. Intelligent Triage: AI classifies files according to the TMF Reference Model and extracts metadata without human prompts. Human-in-the-Loop: Critically, the system includes validation checkpoints to ensure audit traceability and GCP (Good Clinical Practice) compliance. While Medable claims their agents “remove friction,” the real test will be interoperability. If the TMF Agent can seamlessly push data into a competitor’s eTMF without a “proprietary wall,” it will be a major win for biopharma sponsors.
Beyond the Sensor: How January AI’s Food Scanner Replaces CGM Friction for GLP-1 Care
What You Should Know – January AI has transitioned its science-validated “virtual glucose monitor” technology into a suite of Enterprise APIs, enabling B2B partners to embed AI-powered food recognition and glucose prediction into their own platforms. – This move allows health systems and longevity companies to turn underutilized lifestyle signals—like photos and wearable data—into structured, ... Read More
<img loading="lazy" width="878" height="736" src="https://hitconsultant.net/wp-content/uploads/2026/01/unnamed-1.png" alt="" class="wp-image-93694" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/unnamed-1.png 878w, https://hitconsultant.net/wp-content/uploads/2026/01/unnamed-1-300x251.png 300w, https://hitconsultant.net/wp-content/uploads/2026/01/unnamed-1-290x243.png 290w, https://hitconsultant.net/wp-content/uploads/2026/01/unnamed-1-768x644.png 768w" sizes="(max-width: 878px) 100vw, 878px" /> What You Should Know – January AI has transitioned its science-validated “virtual glucose monitor” technology into a suite of Enterprise APIs, enabling B2B partners to embed AI-powered food recognition and glucose prediction into their own platforms. – This move allows health systems and longevity companies to turn underutilized lifestyle signals—like photos and wearable data—into structured, metabolic insights at scale. The “Context Gap”: Why Lifestyle Data Often Fails Modifiable lifestyle behaviors drive an estimated 80–90% of health outcomes, yet this data typically lives outside the clinical record. January AI’s new enterprise solution bridges this gap by transforming everyday inputs into “decision-ready” healthcare applications: AI Vision & Food Scanning: A market-leading scanner that recognizes ingredients and provides detailed macronutrient analysis across 54 million verified food items. Virtual Glucose Prediction: Leveraging generative AI to forecast glycemic responses without the need for invasive sensors or expensive CGMs. Predictive Food Swaps: Recommends personalized alternatives to prevent glucose spikes based on a user’s individual health state and metabolic profile. While January AI boasts “industry-best” food scanning accuracy, skepticism is warranted when dealing with complex, multi-ingredient restaurant meals. However, their commitment to peer-reviewed clinical validation for their glucose curves separates them from the “marketing fluff” typical of the digital nutrition space. Market Forecast: 2026 and Beyond With partners ranging from longevity companies to national institutional health systems, January AI is positioning itself as the metabolic backbone of the “Accountability Phase” of AI. For C-suite leaders, the opportunity lies in integrating these APIs to provide a “360-degree” view of patient health that extends beyond the clinic walls.
The Digital Insider: Securing Agentic AI in the 2026 Healthcare Landscape
Ben Scharfe, EVP for AI, Altera Digital Health
Hospitals have thrown their doors open to a new kind of technology that can think, act and make changes faster than any human. And with healthcare generating more than 30% of the world’s data, the industry has, for better or worse, become the ultimate proving ground for AI, from all of the good it can ... Read More
<img loading="lazy" src="https://hitconsultant.net/wp-content/uploads/2026/01/Ben_Scharfe.jpg" alt="" class="wp-image-93638" width="561" height="701" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/Ben_Scharfe.jpg 900w, https://hitconsultant.net/wp-content/uploads/2026/01/Ben_Scharfe-240x300.jpg 240w, https://hitconsultant.net/wp-content/uploads/2026/01/Ben_Scharfe-232x290.jpg 232w, https://hitconsultant.net/wp-content/uploads/2026/01/Ben_Scharfe-768x960.jpg 768w" sizes="(max-width: 561px) 100vw, 561px" /> Ben Scharfe, EVP for AI, Altera Digital Health Hospitals have thrown their doors open to a new kind of technology that can think, act and make changes faster than any human. And with healthcare generating more than 30% of the world’s data, the industry has, for better or worse, become the ultimate proving ground for AI, from all of the good it can support to all of the threats that it amplifies. AI is being woven into diagnostics, scheduling, documentation and the overarching integration and interoperability plans that make healthcare better. And it’s happening at a faster rate than security and privacy can keep up with. The same technology that drives precision, personalization and efficiency in care also multiplies the entry points for cyberattacks and hackers—and according to a recent HIMSS survey, more than 72% of health leaders report high levels of concern for data privacy risks. Keeping this new healthcare environment safe means rethinking how we interact with and trust AI. Power with consequences According to HIMSS, more than 60% of healthcare professionals value AI for its ability to recognize health patterns and identify potential diagnoses. And more than 86% of respondents have already implemented AI in some way. Agentic AI, in particular, is making its way across the industry without pause. Before AI, there was one tool for every task in healthcare. AI platforms do not fit this existing paradigm, and often a single AI platform may serve a multitude of use cases and interact with multiple systems. The problem is that agentic AI multiplies a healthcare system’s exposure to attacks. Since a single model can touch dozens of systems and move data between them, it creates new vulnerabilities systemwide that security teams can’t always see until something goes wrong. That’s because most users don’t fully understand what agentic AI can do or what permissions it can inherit and take with it through the system. Because agentic AI can make decisions and carry out tasks, these systems can act on behalf of users to perform complex operations and connect tools across health networks automatically. Anything a person can access, the AI can access—unless guardrails are put in place to prevent it—and it can do it faster than humans without fatigue or hesitation. CISOs must treat agentic AI not like a tool or resource, but like any other user or operator. That means managed access, recorded actions and required encryption and multifactor authentication before it touches or manages health information or major system components. Without these guardrails in place, AI agents could pose exponentially more risk than a compromised user. Amplification of threats For every new AI-based tool that promises to modernize healthcare, CISOs have to assume that attackers are adopting the same and building upon it. AI is already powering phishing, ransomware and data-theft campaigns that are faster and more difficult to detect. Cyber attackers now use AI to combine stolen health data with insurance and personal information to craft urgent, personalized messages that lack all the hallmarks of phishing and social engineering campaigns like spelling and grammatical errors. Industrywide, health records remain the most valuable asset on the dark web because they contain enough information to link clinical and financial information with identity. Today, a patient could easily receive a message offering a “cure” based on their very real diagnosis; that’s how easy it is for data to be used against the very people it was meant to help. Ransomware operations are also becoming more sophisticated, helping cybercriminals map networks, locate valuable data and trigger their software automatically. In fact, a recent MIT Sloan survey found that up to 80% of new ransomware attacks are enhanced or run by AI. And a single systemwide vulnerability can be exploited in mere minutes. For CISOs, the reality is clear. Prevention alone isn’t enough. Active monitoring to support early detection and containment and immutable backups to rebuild critical operations without negotiation are key to resilience. ‘Zero trust’ to build trust Hospital systems and security frameworks weren’t built for autonomous systems that have the same privileges as humans. And here, where so much is at stake, zero trust should be the default. This means governance has to extend beyond firewalls and compliance checklists to manage how AI operates and thinks, what data it can access and who is accountable when something goes wrong. Every request for access must be verified continuously and environments segmented to prevent one error or breach from sweeping through the entire system. Every vendor and system must be vetted thoroughly before deployment with explicit clarity on data ownership, prompts, outputs and overrides. All of this takes “free” tools out of the running for use in healthcare as they typically come with the highest cost: exchanging data and control for free software. Real defense isn’t just about having controls; it’s about those controls doing the job well. And those organizations that treat AI security as part of patient safety will be the ones that maintain trust where others lose it. Automation meets fortification Every improvement in AI changes the foundation of healthcare and healthcare security. The same technology that is improving care and patient lives is rewriting the rules of technology defense, and it’s doing it near-daily. Cybercriminals are moving faster than ever, using AI to automate and perfect their attacks. Thus, we’re faced with a reality where it’s no longer about whether we can prevent every breach, but whether we have the tools, resources and protocols in place to get back to work. Healthcare CISOs have the opportunity now to build defenses that withstand what’s coming, and that defense requires three key things: Auditing AI exposure to identify every system, dataset and workflow connected to AI. Rebuilding and strengthening governance to define ownership, access, and accountability for all AI operations before they have access to anything. Reinforcing and building upon defense to ensure that zero-trust segmentation and authentication and immutable backups are the heart of the plan. It’s resilience, not speed, that marks progress. And in this age of agentic AI—and whatever comes next—resilience will depend not on how well hospitals use AI but how well CISOs understand, contain and govern the power AI is given. About Ben Scharfe Ben Scharfe is the EVP for AI at Altera Digital Health. A seasoned leader in the healthcare technology sector, Ben has dedicated more than ten years to fostering growth and innovation at Harris and Altera. His expertise spans financial leadership, client support and executive management, contributing to Harris’ robust expansion, including the successful acquisition and integration of numerous entities. Currently leading AI initiatives, Ben champions the adoption of AI and innovation with the purpose of enhancing patient-provider interactions and patient outcomes.
Designing Trust-Centered Digital Health Systems for Sensitive Patient Interactions
Digital health tools now shape many of the most sensitive moments in the patient experience. Individuals use digital platforms to report concerns, describe symptoms they hesitate to discuss in person, and provide feedback that carries emotional weight. When these exchanges occur in digital environments, system design influences whether users feel supported, dismissed, or uncertain about ... Read More
Digital health tools now shape many of the most sensitive moments in the patient experience. Individuals use digital platforms to report concerns, describe symptoms they hesitate to discuss in person, and provide feedback that carries emotional weight. When these exchanges occur in digital environments, system design influences whether users feel supported, dismissed, or uncertain about how their information will be handled. Trust becomes a central factor in these moments. As digital health expands, systems must be built with a clear understanding of the emotional and practical weight that sensitive interactions often carry. Understanding Sensitive Patient Interactions in Digital Health Sensitive patient interactions arise when individuals share information that involves personal risk or discomfort. These disclosures may relate to symptoms tied to embarrassment or fear, early signs of distress, or concerns about personal safety or the quality of care they received. Digital platforms have become a primary channel for these situations, including patient portals, virtual visits, and structured intake tools. The patient’s comfort level influences the accuracy and completeness of what they choose to share. Systems that feel impersonal or unclear can cause hesitation, which may lead users to omit important details. Systems that feel predictable and respectful encourage more open communication. Treating these interactions as distinct moments within the digital experience is essential for designing tools that respond thoughtfully and effectively. The Stakes of System Behavior in High-Sensitivity Moments Digital design directly shapes how patients experience sensitive exchanges. Interface cues, timing, tone, and workflow structure can influence whether users feel safe or uncertain when entering information that matters to them. A transactional or rigid environment can lead patients to question whether their concerns will receive appropriate attention. Many digital teams optimize for speed or efficiency. Sensitive interactions call for a different standard, one in which steadiness, clarity, and patient awareness guide the design. When digital tools demonstrate this level of care, they foster trust and help individuals communicate with greater confidence. Principles of Trust-Centered Digital System Design Trust begins with clear communication. Systems should explain how patient information will be used, who may review it, and what users can expect after submission. Consistent and straightforward language reassures individuals that their contributions matter. Research on the evolution of care delivery, including the National Academy of Medicine’s analysis of the promise of digital health, highlights how transparency and dependable interactions contribute to stronger user confidence. User control is another essential element of trust-centered design. Patients should be able to decide how much information they want to share, adjust privacy settings when appropriate, and understand when their responses may prompt follow-up actions. These controls help individuals communicate sensitive details with greater clarity and less hesitation. Effective system design also requires structured escalation rules. Digital tools should recognize when specific disclosures call for additional attention. This may involve routing information to the right personnel, prompting clarifying questions, or guiding users into structured workflows that help them communicate without feeling overwhelmed. These practices support patient comfort and create consistency across the digital experience. Building Safe Digital Pathways for Sensitive Disclosures Digital tools often serve as the first point of contact when patients decide to share difficult or personal information. The way a system behaves during these moments can influence whether users complete the disclosure or stop midway. Structured pathways create a sense of steadiness that supports clear communication. Language choices are especially important. Neutral and direct wording can help patients describe experiences that carry emotional weight. Interface decisions also matter. Systems should avoid abrupt transitions, unclear prompts, or visuals that create unnecessary tension. When digital environments feel steady and predictable, patients are more willing to provide accurate and complete information. Digital workflows should also signal attentiveness. When a patient begins to describe discomfort or concern, the platform can guide the process with thoughtful sequencing that gives users time to articulate what happened. This approach supports more accurate reporting and reinforces the system’s recognition of the significance of the moment. Supporting Accountability and Follow-Through in Digital Health Systems Sensitive disclosures often lead to needs that extend beyond the initial interaction. Digital systems should help users understand what happens after they submit information, how reviews occur, and what types of communication they may receive in response. This clarity reduces uncertainty and supports a more stable experience for individuals who may already feel uneasy about sharing personal details. Some disclosures describe experiences involving distress, harm, or safety concerns. In these situations, platforms should acknowledge that patients may benefit from support that reaches beyond clinical staff or internal review teams. Digital tools are more effective when they recognize the wider landscape of resources that patients rely on, including access to legal support for survivors seeking accountability when individuals report events that require further attention by trained professionals. Systems that account for these realities demonstrate a deeper understanding of the complexities of sensitive interactions and reinforce the idea that digital environments should reflect the full range of available support. Responsible Data Practices That Strengthen Institutional Trust Systems that collect sensitive information must demonstrate disciplined data practices. Patients expect their disclosures to be treated with respect, reviewed by appropriate personnel, and protected from unnecessary exposure. Clear data governance policies help organizations define how information moves through the system and what safeguards apply at each point. When users understand how their data is handled, they are more likely to trust the platform. Reliable audit trails add another layer of confidence. They allow organizations to verify that sensitive information was reviewed and processed according to established procedures. These practices enhance accountability and create a structure for responding to disclosures that require specific forms of attention. Responsible approaches to data handling, including emerging methods that reinforce transparency and predictability, align with discussions of thoughtful AI implementation and highlight the importance of consistency across digital systems. Clear communication about data practices encourages more open engagement. When patients believe their information is handled with care and precision, they share details that help systems deliver appropriate follow-through and improve long-term trust. Practical Steps for Technology Teams Technology teams can support sensitive interactions by adopting practices that prioritize clarity and user awareness throughout the development process. Reviewing real examples of sensitive disclosures can help design teams understand where users may hesitate or feel unsure about how their information will be received. These insights can guide decisions about interface language, visual structure, and workflow clarity. Cross-functional collaboration adds depth to this work. Clinicians, compliance experts, engineers, and designers contribute perspectives that help shape systems capable of handling sensitive information with care. Regular evaluation of routing logic, escalation rules, and communication patterns helps ensure platforms remain aligned with organizational standards and user expectations. Testing with a diverse group of participants can reveal points of confusion or discomfort that warrant refinement. Integrating these considerations into the development process helps create digital environments that feel stable and reliable. When teams focus on the quality of the interaction as much as the functionality behind it, they create systems that respond more effectively to complex patient experiences. Conclusion Digital health systems now serve as the setting for many of the most meaningful patient interactions. When individuals choose to share information that carries personal significance, they look for signs that the system will handle it with care and respond constructively. Trust-centered design provides a strong foundation for meeting these expectations by emphasizing clarity, user control, and dependable pathways for follow-through. Platforms that demonstrate this level of attentiveness allow patients to communicate with greater confidence. As digital health continues to evolve, systems built around thoughtful design and responsible data practices will play an important role in supporting patient well-being and strengthening long-term trust.
KLAS Healthcare IoT Security 2026: Top Vendors Rated for Risk Reduction and ROI
What You Should Know: A new 2026 report from KLAS Research signals a major shift in the healthcare IoT security market: hospitals are no longer satisfied with just “seeing” their devices—they demand measurable risk reduction. The report names Asimily and Claroty as the market leaders with the highest energy, driven by their ability to deliver ... Read More
<img loading="lazy" width="1481" height="736" src="https://hitconsultant.net/wp-content/uploads/2026/01/KLAS-Report-Healthcare-IOT-Security-.jpg" alt="KLAS Healthcare IoT Security 2026: Top Vendors Rated for Risk Reduction and ROI" class="wp-image-93676" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/KLAS-Report-Healthcare-IOT-Security-.jpg 1481w, https://hitconsultant.net/wp-content/uploads/2026/01/KLAS-Report-Healthcare-IOT-Security--300x149.jpg 300w, https://hitconsultant.net/wp-content/uploads/2026/01/KLAS-Report-Healthcare-IOT-Security--290x144.jpg 290w, https://hitconsultant.net/wp-content/uploads/2026/01/KLAS-Report-Healthcare-IOT-Security--768x382.jpg 768w, https://hitconsultant.net/wp-content/uploads/2026/01/KLAS-Report-Healthcare-IOT-Security--515x255.jpg 515w" sizes="(max-width: 1481px) 100vw, 1481px" /> Source: KLAS Healthcare IoT Security 2026 What You Should Know: A new 2026 report from KLAS Research signals a major shift in the healthcare IoT security market: hospitals are no longer satisfied with just “seeing” their devices—they demand measurable risk reduction. The report names Asimily and Claroty as the market leaders with the highest energy, driven by their ability to deliver tangible outcomes like automated remediation and strong segmentation. Conversely, legacy players are facing scrutiny over complexity and training gaps as health systems tighten their budgets. For the past five years, the healthcare Internet of Things (IoT) security market was defined by a single buzzword: Visibility. If you could see the device, you could secure it. But in 2026, seeing is no longer enough. According to the newly released Healthcare IoT Security 2026 report by KLAS Research, the market has graduated to a “prove it” phase. Healthcare organizations, squeezed by financial constraints and rising cyber threats, are evaluating vendors based on their ability to deliver “measurable outcomes”—specifically reduced manual effort, accelerated risk remediation, and integration with broader security workflows. The Leaders: Asimily and Claroty In a crowded field, two vendors have separated themselves from the pack with high “Market Energy” and top-tier performance scores. Asimily (Overall Score: 96.6) Asimily has emerged as a favorite for health systems prioritizing value and specific healthcare alignment. The Edge: Customers cite its “risk intelligence” and cost-effectiveness as deciding factors. The Outcome: Users report that the platform significantly reduces manual staff effort by “passively identifying vulnerabilities… and triaging remediation work to the right teams”. ROI: It holds the highest rating for “Money’s Worth” in the report, positioning it as the pragmatic choice for budget-conscious CIOs. Claroty (Overall Score: 92.1) Claroty remains a powerhouse, recognized for its maturity and polish. The Edge: It is viewed as a “mature solution” that integrates seamlessly with existing medical device ecosystems. The Outcome: Users praise its “dependable visibility” and risk scoring, which allows for effective prioritization of remediation tasks. Reliability: Customers describe Claroty as a “strong partner who communicates quickly and transparently”. The “Measurable Outcomes” Mandate The shift in 2026 is driven by economics. As the report notes, “As healthcare organizations continue to grapple with financial constraints, it is essential that they see tangible outcomes”. Vendors like Armis (Score: 91.1) are also delivering here. Armis users highlight that the platform’s straightforward reports “inform interventions and support faster risk reduction”. However, the market is becoming less tolerant of tools that require heavy lifting. While Armis is praised for visibility, some users noted a “learning curve” and a need for simplified patch orchestration. The Complexity Trap: Palo Alto & Forescout While the leaders accelerate, some well-known names are facing headwinds due to the complexity of their solutions. Palo Alto Networks: While praised for its firewall integration, the solution’s complexity has led to mixed reviews. Customers noted that “training and support are costly and sometimes difficult to access”. Forescout Technologies: Similarly, users described the learning curve as “steep” and noted that training was “basic and not healthcare specific”. In 2026, a powerful tool that requires a PhD to operate is a liability. Hospitals are demanding “proactive and role-based training” to ensure their teams can actually use the software they purchased. Future Needs: The 2026 Roadmap Healthcare leaders are currently eyeing three major technical advancements: AI-Driven Automation: Specifically for prioritization and device fingerprinting. Microsegmentation: Moving beyond simple network separation to granular, device-level security. Unified Vulnerability Management: Tying IoT security directly into clinical workflows rather than keeping it in an IT silo. Bottom Line For 2026, the question is no longer “Can we see our devices?” but “Can we protect them without adding headcount?” Choose a vendor like Claroty or Asimily if your priority is mature remediation, but ensure your team is prepared for the technical complexity that comes with these high-performance tools.
The Unbreakable Web: Building the 2026 Hybrid Network Stack for AI-Driven Care
Artificial intelligence (AI) and the Internet of Things (IoT) are transforming healthcare at a pace that few could have predicted. What once were experimental use cases, predictive diagnostics, real-time patient monitoring, and AI-assisted surgeries are now entering everyday clinical practice. Yet there is one foundational element without which none of this progress can endure: the ... Read More
<img loading="lazy" src="https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-1500x1967.jpg" alt="The Unbreakable Web: Building the 2026 Hybrid Network Stack for AI-Driven Care" class="wp-image-93630" width="619" height="812" srcset="https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-1500x1967.jpg 1500w, https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-229x300.jpg 229w, https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-221x290.jpg 221w, https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-768x1007.jpg 768w, https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-1171x1536.jpg 1171w, https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-1562x2048.jpg 1562w, https://hitconsultant.net/wp-content/uploads/2026/01/JulianHeadshot_2025-scaled.jpg 1952w" sizes="(max-width: 619px) 100vw, 619px" /> Julian Jacquez, President & COO, BCN Artificial intelligence (AI) and the Internet of Things (IoT) are transforming healthcare at a pace that few could have predicted. What once were experimental use cases, predictive diagnostics, real-time patient monitoring, and AI-assisted surgeries are now entering everyday clinical practice. Yet there is one foundational element without which none of this progress can endure: the network infrastructure that connects it all. In this new era, where every heartbeat, every pixel of a scan, and every physician’s prompt can flow across a web of devices, facilities, and continents, networks are no longer back-office concerns. They are the central nervous system of modern medicine. The AI + IoT Explosion in Healthcare AI is powering faster diagnoses, adaptive treatment recommendations, and personalized care plans based on genomic and behavioral data. IoT, meanwhile, has brought about a revolution in continuous care from smart inhalers and wearable ECGs to remote infusion pumps and asset-tracking tags in hospitals. Together, these technologies form the Healthcare Internet of Things (H-IoT): a decentralized, data-rich, and care-centric ecosystem. But with these advances come immense challenges, chief among them, how to build a resilient, secure, and responsive network infrastructure that can keep up. Hybrid Network Stack: From Rigid to Resilient Traditional network models, built around centralized data centers and on-premise systems, are inadequate for the demands of modern, mobile, and mission-critical healthcare. The future lies in hybrid network stack solutions, layered architectures that blend multiple connectivity technologies and processing environments. The modern hybrid network stack includes: Edge computing: For ultra-low-latency, real-time analysis at the point of care (e.g., ICUs or ambulances). On-premise servers: For secure handling of regulated patient data and local failover. Cloud platforms: For scalable storage, AI model training, cross-site collaboration, and population health analytics. 4G LTE, 5G: For high-speed device connectivity in dense clinical environments. Software-defined Wide Area Networking (SDWAN): To orchestrate data traffic, optimize QoS, and enforce policy controls. Satellite internet: As a fail-safe layer that ensures continuous connectivity in remote, rural, or disaster-stricken environments. This stack is not only flexible, it’s fault-tolerant. Each layer complements the others, enabling healthcare systems to dynamically shift workloads, reroute data, and maintain continuity of care under any circumstance. The Role of Satellite: Building the “Unbreakable” Network One of the most overlooked but vital components of a hybrid stack is satellite internet. With the growing availability of low-earth orbit (LEO) satellite constellations, healthcare networks can now achieve near-global, high-speed, and low-latency backup connectivity. Why this matters: Remote healthcare delivery: Rural clinics and mobile health units can access cloud AI tools and EMRs without relying on unreliable terrestrial connections. Disaster resilience: Hospitals hit by natural disasters (e.g., hurricanes, earthquakes) can stay online, thanks to satellite redundancy. Uninterrupted telemedicine: When 5G or fiber networks go down, satellite ensures continued access to remote consultations, imaging reviews, and even remote surgeries. By integrating satellite as part of a hybrid stack, not as a last resort, but as a strategic failover layer, healthcare systems become more resilient to everything from cyberattacks to climate disruption. Cybersecurity in a Multi-Layered, Always-On World With great connectivity comes great risk. Healthcare remains the most targeted industry for cyberattacks, and AI and IoT expand the surface area for threats. In a hybrid network environment, cybersecurity must be layered, intelligent, and proactive. Key strategies include: Zero Trust Network Access (ZTNA): Every device, user, and application must verify identity before access. AI-driven anomaly detection: Using machine learning to detect unusual data patterns or device behavior. End-to-end encryption and microsegmentation: Especially important when moving between edge, cloud, and satellite nodes. Continuous patching and firmware updates: Crucial for securing medical IoT devices, many of which operate beyond the IT department’s direct control. Cybersecurity must be embedded across all layers of the hybrid stack, including satellite transmissions, which require specialized encryption and anti-jamming protocols. Interoperability: The Hidden Barrier to Network Intelligence Even the best networks fail if the systems they connect can’t “speak” the same language. Interoperability remains a major roadblock in achieving unified, intelligent healthcare delivery. AI and IoT systems from different vendors often use incompatible data formats, APIs, or transport protocols. That leads to data silos, diagnostic delays, and clinician frustration. To address this, network infrastructure must support: Standardized protocols like HL7 FHIR Intelligent middleware to translate between systems API gateways and edge brokers to streamline integration across cloud, local, and satellite environments In essence, the network should not only move data it must make it usable at every destination. Real-World Examples: What’s Already Happening Virtual ICUs: Powered by edge AI and SDN, allowing critical care specialists to monitor patients across multiple hospitals with real-time data streams and alerts. Connected ambulances: Using 5G and satellite to relay patient vitals and live video en route to emergency departments. Disaster-proof clinics: Rural outposts using hybrid stacks with edge, local servers, and Starlink-based satellite backup to stay online through outages. These are not just proof of concepts, they are prototypes for the global hospital of the future. The Big Shift: From Network-as-Utility to Network-as-Clinical Asset As AI and IoT become fundamental to healthcare delivery, networks can no longer be viewed as IT plumbing. They must be treated as clinical infrastructure as critical to patient safety and outcomes as any drug or device. Investing in hybrid stacks, with redundancy, intelligence, and security at every layer, is not a luxury. It’s the foundation for healthcare that is resilient, equitable, and future-ready. Final Thought: Healthcare’s Digital Nervous System Must Be Unbreakable The future of medicine will be defined not only by the brilliance of AI or the precision of sensors, but by the invisible web that binds them. This web must be fast, secure, flexible and above all, unbreakable. Hybrid network stacks, reinforced by satellite connectivity and AI-enhanced security, are the only path forward. They are how we ensure that, regardless of a patient’s location, whether in a rural village or a megacity, on stable ground or in a disaster zone, healthcare data flows, decisions are made, and lives are saved. Because in tomorrow’s healthcare, connectivity is care. About Julian Jacquez, Jr. Julian Jacquez, Jr. joined BCN in 2004 and delivers years of experience in senior executive leadership and strategic guidance at BCN. In June 2018, Mr. Jacquez began serving as President of BCN in addition to his role as Chief Operating Officer. As President and COO, Mr. Jacquez oversees sales, marketing, offer management, and operations for BCN, as well as the company’s CRM, billing, business support systems, and corporate IT infrastructure. Additionally, Mr. Jacquez is actively involved in the development and management of BCN’s nationwide partner-based distribution channel, and its alignment with compensation and reward programs for BCN employee groups. Prior to BCN, Mr. Jacquez held a range of financial, management, and ownership positions at other telecom service providers.